172 lines
6.3 KiB
Python
172 lines
6.3 KiB
Python
import aiohttp
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import aiofiles
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import ujson
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import orjson
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import sqlite3
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import asyncio
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import pandas as pd
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import time
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import os
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from dotenv import load_dotenv
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from datetime import datetime, timedelta
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from tqdm import tqdm
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import pytz
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headers = {"accept": "application/json"}
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url = "https://api.benzinga.com/api/v2.1/calendar/earnings"
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load_dotenv()
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api_key = os.getenv('BENZINGA_API_KEY')
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ny_tz = pytz.timezone('America/New_York')
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today = datetime.now(ny_tz).replace(hour=0, minute=0, second=0, microsecond=0)
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min_date = ny_tz.localize(datetime.strptime("2020-01-01", "%Y-%m-%d"))
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N_days_ago = today - timedelta(days=10)
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async def save_json(data, symbol, dir_path):
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file_path = os.path.join(dir_path, f"{symbol}.json")
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async with aiofiles.open(file_path, 'w') as file:
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await file.write(ujson.dumps(data))
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from datetime import datetime, timedelta
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import pytz
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ny_tz = pytz.timezone("America/New_York")
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async def calculate_price_reactions(filtered_data, price_history):
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# Ensure price_history is sorted by date
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price_history.sort(key=lambda x: datetime.strptime(x['time'], "%Y-%m-%d"))
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# Convert price history to a dictionary for quick lookup
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price_dict = {entry['time']: entry for entry in price_history}
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results = []
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for earnings in filtered_data:
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report_date = earnings['date']
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report_datetime = ny_tz.localize(datetime.strptime(report_date, "%Y-%m-%d"))
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# Initialize a dictionary for price reactions
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price_reactions = {'date': report_date, 'quarter': earnings['quarter'], 'year': earnings['year']}
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for offset in [0,1,2]: # Days around earnings
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# Calculate initial target date with offset
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target_date = report_datetime - timedelta(days=offset)
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# Adjust target_date to the latest weekday if it falls on a weekend
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if target_date.weekday() == 5: # Saturday
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target_date -= timedelta(days=1) # Move to Friday
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elif target_date.weekday() == 6: # Sunday
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target_date -= timedelta(days=2) # Move to Friday
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target_date_str = target_date.strftime("%Y-%m-%d")
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while target_date_str not in price_dict: # Ensure target_date exists in price_dict
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target_date -= timedelta(days=1)
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target_date_str = target_date.strftime("%Y-%m-%d")
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price_data = price_dict[target_date_str]
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# Find the previous day's price data
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previous_date = target_date - timedelta(days=1)
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if previous_date.weekday() == 5: # Saturday
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previous_date -= timedelta(days=1) # Move to Friday
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elif previous_date.weekday() == 6: # Sunday
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previous_date -= timedelta(days=2) # Move to Friday
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previous_date_str = previous_date.strftime("%Y-%m-%d")
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while previous_date_str not in price_dict: # Ensure previous_date exists in price_dict
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previous_date -= timedelta(days=1)
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previous_date_str = previous_date.strftime("%Y-%m-%d")
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previous_price_data = price_dict[previous_date_str]
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# Calculate close price and percentage change
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price_reactions[f"{offset+1}_days_close"] = price_data['close']
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price_reactions[f"{offset+1}_days_change_percent"] = round(
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(price_data['close'] / previous_price_data['close'] - 1) * 100, 2
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)
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print(target_date_str, previous_date_str)
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results.append(price_reactions)
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return results
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async def get_past_data(data, ticker, con):
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# Filter data based on date constraints
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filtered_data = []
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for item in data:
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try:
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item_date = ny_tz.localize(datetime.strptime(item["date"], "%Y-%m-%d"))
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if min_date <= item_date <= today:
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filtered_data.append(
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{
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'revenue': float(item['revenue']),
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'revenueEst': float(item['revenue_est']),
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'revenueSurprisePercent': round(float(item['revenue_surprise_percent'])*100, 2),
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'eps': round(float(item['eps']), 2),
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'epsEst': round(float(item['eps_est']), 2),
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'epsSurprisePercent': round(float(item['eps_surprise_percent'])*100, 2),
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'year': item['period_year'],
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'quarter': item['period'],
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'date': item['date']
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}
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)
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except:
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pass
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# Sort the filtered data by date
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if len(filtered_data) > 0:
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filtered_data.sort(key=lambda x: x['date'], reverse=True)
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try:
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# Load the price history data
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with open(f"json/historical-price/max/{ticker}.json") as file:
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price_history = orjson.loads(file.read())
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results = await calculate_price_reactions(filtered_data, price_history)
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print(filtered_data[0])
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print(results[1])
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# Save the updated filtered_data
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#await save_json(filtered_data, ticker, 'json/earnings/past')
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except:
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pass
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async def get_data(session, ticker, con):
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querystring = {"token": api_key, "parameters[tickers]": ticker}
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try:
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async with session.get(url, params=querystring, headers=headers) as response:
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data = ujson.loads(await response.text())['earnings']
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await get_past_data(data, ticker, con)
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except Exception as e:
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print(e)
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#pass
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async def run(stock_symbols, con):
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async with aiohttp.ClientSession() as session:
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tasks = [get_data(session, symbol, con) for symbol in stock_symbols]
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for f in tqdm(asyncio.as_completed(tasks), total=len(stock_symbols)):
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await f
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try:
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con = sqlite3.connect('stocks.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND symbol NOT LIKE '%-%'")
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stock_symbols = [row[0] for row in cursor.fetchall()]
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stock_symbols = ['AMD']
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asyncio.run(run(stock_symbols, con))
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except Exception as e:
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print(e)
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finally:
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con.close() |